Using score differences for search result diversification

We investigate the application of a light-weight approach to result list clustering for the purposes of diversifying search results. We introduce a novel post-retrieval approach, which is independent of external information or even the full-text content of retrieved documents; only the retrieval score of a document is used. Our experiments show that this novel approach is bene cial to e ectiveness, albeit only on certain baseline systems. The fact that the method works indicates that the retrieval score is potentially exploitable in diversity

On the suitability of intent spaces for IR diversification

This is an electronic version of the paper presented at the International Workshop on Diversity in Document Retrieval (DDR 2012), held in Seattle on 2012Recent developments in Information Retrieval diversity are based on the consideration of a space of information need aspects, a notion which takes different forms in the literature. The choice of a suitable aspect space for diversification is a critical issue when designing an IR diversification strategy, which has not been explicitly addressed to some depth in the literature. This paper aims to identify relevant properties of the aspect space which may help the system designer in making a suitable choice in selecting and configuring this space, and diagnosing malfunctions of the diversification algorithms. In particular, we identify the mutual information between aspects and documents as a meaningful magnitude, in terms of which anomalous cases can be characterized. We further seek to discern favorable cases through a combination of theoretic and empirical analysis.This work is supported by the Spanish Government (TIN2011-28538-C02-01), and the Government of Madrid (S2009TIC-1542)

Explicit relevance models in intent-oriented information retrieval diversification

This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval, http://dx.doi.org/10.1145/2348283.2348297.The intent-oriented search diversification methods developed in the field so far tend to build on generative views of the retrieval system to be diversified. Core algorithm components in particular redundancy assessment are expressed in terms of the probability to observe documents, rather than the probability that the documents be relevant. This has been sometimes described as a view considering the selection of a single document in the underlying task model. In this paper we propose an alternative formulation of aspect-based diversification algorithms which explicitly includes a formal relevance model. We develop means for the effective computation of the new formulation, and we test the resulting algorithm empirically. We report experiments on search and recommendation tasks showing competitive or better performance than the original diversification algorithms. The relevance-based formulation has further interesting properties, such as unifying two well-known state of the art algorithms into a single version. The relevance-based approach opens alternative possibilities for further formal connections and developments as natural extensions of the framework. We illustrate this by modeling tolerance to redundancy as an explicit configurable parameter, which can be set to better suit the characteristics of the IR task, or the evaluation metrics, as we illustrate empirically.This work was supported by the national Spanish projects TIN2011-28538-C02-01 and S2009TIC-1542

Interactive Intent Modeling for Exploratory Search

Exploratory search requires the system to assist the user in comprehending the information space and expressing evolving search intents for iterative exploration and retrieval of information. We introduce interactive intent modeling, a technique that models a user’s evolving search intents and visualizes them as keywords for interaction. The user can provide feedback on the keywords, from which the system learns and visualizes an improved intent estimate and retrieves information. We report experiments comparing variants of a system implementing interactive intent modeling to a control system. Data comprising search logs, interaction logs, essay answers, and questionnaires indicate significant improvements in task performance, information retrieval performance over the session, information comprehension performance, and user experience. The improvements in retrieval effectiveness can be attributed to the intent modeling and the effect on users’ task performance, breadth of information comprehension, and user experience are shown to be dependent on a richer visualization. Our results demonstrate the utility of combining interactive modeling of search intentions with interactive visualization of the models that can benefit both directing the exploratory search process and making sense of the information space. Our findings can help design personalized systems that support exploratory information seeking and discovery of novel information.Peer reviewe

Supervised approaches for explicit search result diversification

Diversification of web search results aims to promote documents with diverse content (i.e., covering different aspects of a query) to the top-ranked positions, to satisfy more users, enhance fairness and reduce bias. In this work, we focus on the explicit diversification methods, which assume that the query aspects are known at the diversification time, and leverage supervised learning methods to improve their performance in three different frameworks with different features and goals. First, in the LTRDiv framework, we focus on applying typical learning to rank (LTR) algorithms to obtain a ranking where each top-ranked document covers as many aspects as possible. We argue that such rankings optimize various diversification metrics (under certain assumptions), and hence, are likely to achieve diversity in practice. Second, in the AspectRanker framework, we apply LTR for ranking the aspects of a query with the goal of more accurately setting the aspect importance values for diversification. As features, we exploit several pre- and post-retrieval query performance predictors (QPPs) to estimate how well a given aspect is covered among the candidate documents. Finally, in the LmDiv framework, we cast the diversification problem into an alternative fusion task, namely, the supervised merging of rankings per query aspect. We again use QPPs computed over the candidate set for each aspect, and optimize an objective function that is tailored for the diversification goal. We conduct thorough comparative experiments using both the basic systems (based on the well-known BM25 matching function) and the best-performing systems (with more sophisticated retrieval methods) from previous TREC campaigns. Our findings reveal that the proposed frameworks, especially AspectRanker and LmDiv, outperform both non-diversified rankings and two strong diversification baselines (i.e., xQuAD and its variant) in terms of various effectiveness metrics

Explicit web search result diversification

Queries submitted to a web search engine are typically short and often ambiguous. With the enormous size of the Web, a misunderstanding of the information need underlying an ambiguous query can misguide the search engine, ultimately leading the user to abandon the originally submitted query. In order to overcome this problem, a sensible approach is to diversify the documents retrieved for the user's query. As a result, the likelihood that at least one of these documents will satisfy the user's actual information need is increased. In this thesis, we argue that an ambiguous query should be seen as representing not one, but multiple information needs. Based upon this premise, we propose xQuAD---Explicit Query Aspect Diversification, a novel probabilistic framework for search result diversification. In particular, the xQuAD framework naturally models several dimensions of the search result diversification problem in a principled yet practical manner. To this end, the framework represents the possible information needs underlying a query as a set of keyword-based sub-queries. Moreover, xQuAD accounts for the overall coverage of each retrieved document with respect to the identified sub-queries, so as to rank highly diverse documents first. In addition, it accounts for how well each sub-query is covered by the other retrieved documents, so as to promote novelty---and hence penalise redundancy---in the ranking. The framework also models the importance of each of the identified sub-queries, so as to appropriately cater for the interests of the user population when diversifying the retrieved documents. Finally, since not all queries are equally ambiguous, the xQuAD framework caters for the ambiguity level of different queries, so as to appropriately trade-off relevance for diversity on a per-query basis. The xQuAD framework is general and can be used to instantiate several diversification models, including the most prominent models described in the literature. In particular, within xQuAD, each of the aforementioned dimensions of the search result diversification problem can be tackled in a variety of ways. In this thesis, as additional contributions besides the xQuAD framework, we introduce novel machine learning approaches for addressing each of these dimensions. These include a learning to rank approach for identifying effective sub-queries as query suggestions mined from a query log, an intent-aware approach for choosing the ranking models most likely to be effective for estimating the coverage and novelty of multiple documents with respect to a sub-query, and a selective approach for automatically predicting how much to diversify the documents retrieved for each individual query. In addition, we perform the first empirical analysis of the role of novelty as a diversification strategy for web search. As demonstrated throughout this thesis, the principles underlying the xQuAD framework are general, sound, and effective. In particular, to validate the contributions of this thesis, we thoroughly assess the effectiveness of xQuAD under the standard experimentation paradigm provided by the diversity task of the TREC 2009, 2010, and 2011 Web tracks. The results of this investigation demonstrate the effectiveness of our proposed framework. Indeed, xQuAD attains consistent and significant improvements in comparison to the most effective diversification approaches in the literature, and across a range of experimental conditions, comprising multiple input rankings, multiple sub-query generation and coverage estimation mechanisms, as well as queries with multiple levels of ambiguity. Altogether, these results corroborate the state-of-the-art diversification performance of xQuAD

Grundlagen der Informationswissenschaft

Die 7. Ausgabe der "Grundlagen der praktischen Information und Dokumentation" (Erstausgabe 1972) heißt jetzt: „Grundlagen der Informationswissenschaft". Der Bezug zur Praxis und zur Ausbildung bleibt erhalten, aber der neue Titel trägt dem Rechnung, dass die wissenschaftliche theoretische Absicherung für alle Bereiche von Wissen und Information, nicht nur in der Fachinformation, sondern auch in den Informationsdiensten des Internet immer wichtiger wird. Für die Grundlagen sind 73 Artikel in 6 Hauptkapiteln vorgesehen. Viele Themen werden zum ersten Mal behandelt, z.B. Information und Emotion, Informationelle Selbstbestimmung, Informationspathologien. Alle Beiträge sind neu verfasst

On the role of novelty for search result diversification

Re-ranking the search results in order to promote novel ones has traditionally been regarded as an intuitive diversification strategy. In this paper, we challenge this common intuition and thoroughly investigate the actual role of novelty for search result diversification, based upon the framework provided by the diversity task of the TREC 2009 and 2010 Web tracks. Our results show that existing diversification approaches based solely on novelty cannot consistently improve over a standard, non-diversified baseline ranking. Moreover, when deployed as an additional component by the current state-of-the-art diversification approaches, our results show that novelty does not bring significant improvements, while adding considerable efficiency overheads. Finally, through a comprehensive analysis with simulated rankings of various quality, we demonstrate that, although inherently limited by the performance of the initial ranking, novelty plays a role at breaking the tie between similarly diverse results